Estimating the Temporal Evolution of Synaptic Weights from Dynamic Functional Connectivity
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Estimating the Temporal Evolution of Synaptic Weights from Dynamic Functional Connectivity. / Celotto, Marco; Lemke, Stefan; Panzeri, Stefano.
Brain Informatics. ed. / Mufti Mahmud; Jing He; Stefano Vassanelli; André van Zundert; Ning Zhong. Vol. 13406 Cham : Springer, Cham, 2022. p. 3-14 (Lecture Notes in Artificial Intelligence (LNAI)).Research output: SCORING: Contribution to book/anthology › Conference contribution - Article for conference › Research › peer-review
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TY - CHAP
T1 - Estimating the Temporal Evolution of Synaptic Weights from Dynamic Functional Connectivity
AU - Celotto, Marco
AU - Lemke, Stefan
AU - Panzeri, Stefano
PY - 2022/8/20
Y1 - 2022/8/20
N2 - How to capture the temporal evolution of synaptic weights from measures of dynamic functional connectivity (DFC) between the activity of different simultaneously recorded neurons is an important and open problem in systems neuroscience. To address this issue, we first simulated models of recurrent neural networks of spiking neurons that had a spike-timing-dependent plasticity mechanism generating time-varying synaptic and functional coupling. We then used these simulations to test analytical approaches that relate dynamic functional connectivity to time-varying synaptic connectivity. We investigated how to use different measures of directed DFC, such as cross-covariance and transfer entropy, to build algorithms that infer how synaptic weights evolve over time. We found that, while both cross-covariance and transfer entropy provide robust estimates of structural connectivity and communication delays, cross-covariance better captures the evolution of synaptic weights over time. We also established how leveraging estimates of connectivity derived from entire simulated recordings could further boost the estimation of time-varying synaptic weights from the DFC. These results provide useful information to estimate accurately time variations of synaptic strength from spiking activity measures.
AB - How to capture the temporal evolution of synaptic weights from measures of dynamic functional connectivity (DFC) between the activity of different simultaneously recorded neurons is an important and open problem in systems neuroscience. To address this issue, we first simulated models of recurrent neural networks of spiking neurons that had a spike-timing-dependent plasticity mechanism generating time-varying synaptic and functional coupling. We then used these simulations to test analytical approaches that relate dynamic functional connectivity to time-varying synaptic connectivity. We investigated how to use different measures of directed DFC, such as cross-covariance and transfer entropy, to build algorithms that infer how synaptic weights evolve over time. We found that, while both cross-covariance and transfer entropy provide robust estimates of structural connectivity and communication delays, cross-covariance better captures the evolution of synaptic weights over time. We also established how leveraging estimates of connectivity derived from entire simulated recordings could further boost the estimation of time-varying synaptic weights from the DFC. These results provide useful information to estimate accurately time variations of synaptic strength from spiking activity measures.
U2 - 10.1007/978-3-031-15037-1_1
DO - 10.1007/978-3-031-15037-1_1
M3 - Conference contribution - Article for conference
SN - 978-3-031-15036-4
VL - 13406
T3 - Lecture Notes in Artificial Intelligence (LNAI)
SP - 3
EP - 14
BT - Brain Informatics
A2 - Mahmud, Mufti
A2 - He, Jing
A2 - Vassanelli, Stefano
A2 - van Zundert, André
A2 - Zhong, Ning
PB - Springer, Cham
CY - Cham
ER -
